Abstract
This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.
Highlights
As a result of the SARS-CoV-2 pandemic, many hospitals across the world have resorted to drastic measures: canceling elective procedures, switching to remote consultations, designating most beds to COVID-19, expanding Intensive Care Unit (ICU) capacity, and re-purposing doctors and nurses to support COVID-19 care
ICU admission and mechanical ventilation were determined for each patient
We report the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) and the Weighted-F1 score, both computed out-of-sample
Summary
As a result of the SARS-CoV-2 pandemic, many hospitals across the world have resorted to drastic measures: canceling elective procedures, switching to remote consultations, designating most beds to COVID-19, expanding Intensive Care Unit (ICU) capacity, and re-purposing doctors and nurses to support COVID-19 care. A string of recent studies developed models to predict severe disease or mortality based on clinical and laboratory findings, for example (Yan et al, 2020) (n = 485), (Gong et al, 2020) (n = 372), (Bhargava et al, 2020) (n = 197), (Ji et al, 2020) (n = 208), and (Wang et al, 2020) (n = 296) In these studies, several variables such as Lactate Dehydrogenase (LDH) (Gong et al, 2020; Ji et al, 2020; Yan et al, 2020) and C-reactive protein (CRP) have been identified as important predictors.
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